Combining semiparametric regression and kriging for prediction of PM2.5 pollutant levels at unmonitored locations with meterological and traffic data

Abstract

Particulate matter (PM) is defined by the Texas Commission on Environmental Quality (TCEQ) as "a mixture of solid particles and liquid droplets found in the air". These particles vary widely in size. Those particles that are less than 2.5 micrometers in aerodynamic diameter are known as Particulate Matter 2.5 or PM2.5. These particles are inhaled, and their health effects are still largely being studied. Past studies have assessed PM2.5 exposure of a population, yet individual exposure is more difficult to assess and may vary widely in a population. Recent studies have combined semiparametric models with kriging (Li et. al [2012]) to assess nitrogen dioxide exposure in California. These methods may prove valuable at predicting PM2.5 at unmonitored locations in El Paso and subsequently in assessing personal exposure to PM2.5 within our population.^ Garcia (2010) provides us with a unique opportunity to estimate the spatial covariance of PM2.5 in the El Paso region. Past studies have established that PM2.5 varies spatially within a region based on local traffic variables (Smith et. al [2006]). Other studies have found meterological, variables such as wind speed play an important role in PM levels (Staniswalis et al.[2005]). First, we use meteorological variables to build a semiparametric model to estimate the mean PM2.5 at two monitored locations. Then in conjunction with traffic data and the spatial covariance structure of PM2.5, we use kriging of the residuals of the semiparametric models to predict PM2.5 at unmonitored locations.^